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SAMD13 as a Novel Prognostic Biomarker and its Correlation with Infiltrating Immune Cells in Hepatocellular Carcinoma
Biomed Sci Letters 2022;28:260-275
Published online December 31, 2022;  https://doi.org/10.15616/BSL.2022.28.4.260
© 2022 The Korean Society For Biomedical Laboratory Sciences.

Hye-Ran Kim1,* , Choong Won Seo1,* , Jae-Ho Lee2,* , Sang Jun Han3,†,* and Jongwan Kim1,†,*

1Department of Biomedical Laboratory Science, Dong-Eui Institute of Technology, Busan 47230, Korea
2Department of Anatomy, Keimyung University School of Medicine, Daegu 42601, Korea
3Department of Biotechnology, College of Fisheries Sciences, Pukyong National University, Busan 48513, Korea
Correspondence to: Jongwan Kim. Department of Biomedical Laboratory Science, Dong-Eui Institute of Technology, 54 Yangji-ro, Busanjin-gu, Busan 47230, Korea.
Tel: +82-51-860-3525, Fax: +82-51-860-3150, e-mail: dahyun@dit.ac.kr
Sang Jun Han. Department of Biotechnology, College of Fisheries Sciences, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Korea.
Tel: +82-51-629-5862, Fax: +82-51-629-5863, e-mail: sjhan@pknu.ac.kr
*Professor.
Received November 9, 2022; Accepted December 15, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
Sterile alpha motif (SAM) domains bind to various proteins, lipids, and RNAs. However, these domains have not yet been analyzed as prognostic biomarkers. In this study, SAM domain containing 13 (SAMD13), a member of the SAM domain, was evaluated to identify a novel prognostic biomarker in various human cancers, including hepatocellular carcinoma (HCC). Moreover, we identified a correlation between SAMD13 expression and immune cell infiltration in HCC. We performed bioinformatics analysis using online databases, such as Tumor Immune Estimation Resource, UALCAN, Kaplan-Meier plotter, LinkedOmics, and Gene Expression Profiling Interactive Analysis2. SAMD13 expression in HCC samples was significantly higher than that in normal liver tissue; additionally, SAMD13 was higher in primary tumors, various stages of cancer and grades of tumor, and status of nodal metastasis. Higher SAMD13 expression was also associated with poorer prognosis. SAMD13 expression positively correlated with CD8+ T cells, CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells. In the analysis of SAMD13 co-expression networks, positively related genes of SAMD13 were associated with a high hazard ratio in different types of cancer, including HCC. In biological function of SAMD13, SAMD13 mainly include spliceosome, ribosome biogenesis in eukaryote, ribosome, etc. These results suggest that SAMD13 may serve as a novel prognostic biomarker for HCC diagnosis and provide novel insights into tumor immunology in HCC.
Keywords : Hepatocellular carcinoma, SAMD13, Prognosis, Biomarker, Immune cells
INTRODUCTION

Hepatocellular carcinoma (HCC) is currently one of the most frequent malignant tumors worldwide and the second leading cause of cancer-related deaths (Bray et al., 2018; Piñero et al., 2020). HCC accounts for 80% (main type) of all malignant primary liver cancers. HCC is caused by various risk factors such as excessive alcohol consumption, obesity, smoking, type 2 diabetes, and chronic hepatitis B and C virus infections (Gomes et al., 2013; Singal and El-Serag, 2015). Since most patients with HCC are asymptomatic, its diagnosis is challenging in the early stages and mainly diagnosed at an advanced stage with poor prognosis owing to cancer progression (Ling et al., 2014). Surgical resection and drugs, which are conventional treatment methods for HCC, are not effective in most patients, and the prognosis is poor owing to frequent recurrence (Gu et al., 2020). The 5-year survival rate of patients with HCC is approximately 3~5%, and early diagnosis of HCC is essential to increase the survival rate (Yu, 2016). Therefore, it is necessary to develop a new biomarker to increase the early diagnosis and survival rate of patients with HCC. Additionally, to create an effective treatment strategy, research on the molecular etiology of HCC must be conducted. Therefore, the discovery of prognostic biomarkers is very important for the diagnosis and treatment of patients with HCC.

The immune system plays a major role in controlling the progression of cancer, and among them, the roles of the tumor immune microenvironment (TIME) and tumor-infiltrating immune cells (TIICs) are drawing attention (Gentles et al., 2015; Schreiber et al., 2011). The TIME in HCC consists of immune cells such as neutrophils, macrophages, dendritic cells, CD4+ T cells, CD8+ T cells, natural killer cells, and B cells, which play an important role in tumor progression in HCC. In addition, it plays an important role in predicting the prognosis of patients with HCC (Soo et al., 2018). TIME is correlated with cancer incidence and is also regulated by TIICs (Lazăr et al., 2018). Thus far, TIICs have been used as a predictor of clinical outcomes in treating cancer (Bense et al., 2016; Zhang et al., 2019). Therefore, the results of the patient's showing accumulated TIICs play an important role as prognostic indicators.

The sterile alpha motif (SAM) domain is a protein found in the eukaryotic genome that creates large protein complexes in cells and binds to various proteins, lipids, and RNAs (Ray et al., 2020). Certain SAM domains have been identified as functional. SAM domain 9 is responsible for regulating cell proliferation and inhibiting neoplasms. SAM domain 5 expression is associated with the regulation of the cell cycle of cholangiocarcinoma (CC) cells in vitro (Li et al., 2017; Yagai et al., 2017). SAM domains have been studied in cancer cells; however, their function remains unknown, and it is necessary to confirm their potential as new biomarkers for predicting prognosis related to cancer. Currently, studies on biomarkers for improving the prognosis of patients with HCC are continuously being conducted, and some studies have been conducted on the function of HCC prognosis and TIICs. This study is the first that aims to confirm the effectiveness of SAMD13 as a biomarker for HCC.

In this study, we used the Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis2 (GEPIA2), and Kaplan-Meier (KM) plotter database programs to analyze the correlation between SAMD13 expression in patients with HCC and investigated the correlation between SAMD13 and TIICs. Additionally, we identified a joint expression network with SAMD13 using LinkedOmics. Our results potentially seek to uncover strategies for the diagnosis and treatment of HCC by using SAMD13.

MATERIALS AND METHODS

Tumor immune estimation resource database analysis

TIMER has been used to systematically analyze TIICs in diverse cancer types (Li et al., 2017). The TIMER systematic database includes >10,000 tumor samples across 32 cancer types from The Cancer Genome Atlas (TCGA). The expression of SAMD13 has been studied in various cancer types. We also determined the correlation between SAMD13 and infiltrating immune cells (CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, and B cells) in HCC.

OSlihc analysis

The prognostic value of SAMD13 was confirmed using OSlihc (An et al., 2020). To evaluate the prognostic value of genes in OSlihc, survival terms including overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS) were generated, and OS was measured in all cohorts and combined cohorts, while DFI, PFI, and DSS were analyzed using TCGA.

UALCAN database analysis

UALCAN uses TCGA level 3 RNA sequence and clinical data from >30 cancer types (Chandrashekar et al., 2017), allowing the analysis of the relative expression of genes across tumor and normal samples as well as in various tumor subgroups based on individual cancer stages, tumor grades, or other clinicopathological features.

Kaplan-Meier plotter database analysis

The KM database is based on an online database (Györffy et al., 2010). It can estimate the effect of >54,000 genes on survival using >10,000 cancer samples and can be used to confirm the association of genes with survival in various types of cancer, including HCC. KM includes survival rates (OS, relapse-free survival [RFS], progression-free survival [PFS], and DSS) and clinicopathological characteristics data (sex, race, stage, grade, AJCC_T, vascular invasion, alcohol consumption, and hepatitis virus) in HCC. The correlations between SAMD13 and survival rates were identified and presented with the hazard ratio (HR), 95% confidence intervals, and the log rank P-value was computed.

LinkedOmics database analysis

The LinkedOmics database is a platform for analyzing TCGA cancer-related multi-dimensional datasets (Vasaikar et al., 2018). Genes co-expressed with SAMD13 were statistically represented using Pearson's correlation coefficient and presented as heat maps or scatter plots. The function module of LinkedOmics analyzes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways along with transcription factor-target enrichment by gene set enrichment analysis. The rank criteria were FDR<0.05, and 500 simulations were performed.

Gene expression profiling interactive analysis version 2

The GEPIA2 database is an interactive web that includes >9,700 tumor samples and >8,500 normal tissue samples from TCGA and Genotype-Tissue Expression (GTEx) projects (Tang et al., 2017). GEPIA2 was used to generate survival curves, such as OS and DFS, based on the gene expression levels in 33 cancer types. GEPIA2 provides heat maps based on survival outcomes in different types of cancer (Tang et al., 2019). Heatmaps of OS and DFS based on SAMD13 expression across TCGA cancer types were obtained using the "Survival Map". GEPIA2 showed survival curves based on SAMD-13 expression using the log-rank test and Mantel-Cox test.

Statistical analysis

In this study, all data were derived from an open database, and all analyses were confirmed using web tools. All results are expressed as P-values of the log-rank test. Statistical significance was set at P<0.05.

RESULTS

mRNA expression levels of SAMD13 in HCC and different tumor types

To determine the differences in SAMD13 mRNA expression between tumor and normal tissues, SAMD13 expression in normal tissues and different tumor types, including HCC, was studied using TIMER. The expression levels were higher in HCC, glioblastoma multiforme (GBM), kidney renal papillary cell carcinoma (KIRP), liver HCC (LIHC), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), and head and neck squamous cell carcinoma, human papillomavirus (HNSC-HPV+) than in normal tissues. Nevertheless, the expression levels of SAMD13 were lower in breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), pheochromocytoma and paraganglioma (PCPG), rectum adenocarcinoma (READ), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) than in normal tissues (Fig. 1A). Based on these results, the comparison of primary tumors and healthy tissue samples, pathological staging, tumor grade, and nodal metastasis status were analyzed using UALCAN. The expression of SAMD13 in LIHC samples was higher than that in normal liver tissue (Fig. 1B). For individual cancer stages, the expression of SAMD13 in stages 1, 2, 3, and 4 was higher than that in the normal liver tissues (Fig. 1C). In addition, the expression of SAMD13 in grades 1, 2, 3, and 4 was higher than that in the normal liver tissues (Fig. 1D). Regarding nodal metastasis status, the expression of SAMD-13 in N0 was higher than that in normal liver tissues (Fig. 1E).

Fig. 1. SAMD13 expression in various types of cancer. (A) High or low expression of SAMD13 in diverse human tumor compared with the normal tissues. The expression of SAMD13 in HCC (B), cancer stage (C), tumor grade (D), and lymph nodal metastasis status (E) compared with the normal tissues.

Comparison of survival curves of SAMD13 expression in HCC

We investigated whether SAMD13 expression correlated with HCC prognosis. Therefore, the effect of SAMD13 expression on survival rates was evaluated using the KM plotter, OSlihc web server, GEPIA database, and PrognoScan databases. Survival rates, such as OS, RFS, PFS, and DSS, of SAMD13 in HCC were analyzed. The findings revealed that patients with high SAMD13 expression had significantly shorter survival times than those with low expression (Fig. 2A). High SAMD13 expression was associated with poor prognosis in HCC (OS, HR = 1.86, P = 0.00039; RFS, HR = 1.47, P = 0.022; PFS, HR = 1.51, P = 0.0057; DSS, HR = 2.15, P = 0.00063). In addition, higher gene expression of SAMD13 was correlated with poorer prognosis in the OSlihc web server (OS, HR = 2.0584, P = 1e-04; DFI, HR = 1.4872, P = 0.0098; PFI, HR = 1.4769, P = 0.0099; DSS, HR = 2.344, P = 3e-04) (Fig. 2B). Higher SAMD13 expression was also associated with poorer OS (HR = 2.3, P = 4.5e-06) and DFS (HR = 1.4, P = 0.017) (Fig. 2C). These findings demonstrate the prognostic significance of SAMD-13 in HCC.

Fig. 2. The prognostic significance of high gene expression of SAMD13 in HCC. The prognostic significance of SAMD-13 was analyzed using the Kaplan-Meier plotter database (A) Oslihc (B), and TIMER database (C). Overall survival, OS; Relapse free survival, RFS; progression free survival, PFS; Disease specific survival, DSS; progression free interval, PFI; disease free interval, DFI.

Relationship between SAMD13 expression level and clinicopathological characteristics of HCC

We investigated the correlation between SAMD13 expression and clinicopathological characteristics of HCC. Higher SAMD13 expression correlated with poorer prognosis in the following clinical features: sex (HR = 1.37, P = 0.000123), race (HR = 1.37, P = 0.000171), age (HR = 1.37, P = 0.000121), and stage (HR = 1.41, P = 0.000161) (Fig. 3). Higher SAMD13 expression correlated with poorer OS in male (HR = 2.63, P = 3e-05), stage I + II (HR = 1.82, P = 0.014), stage II (HR = 2.7, P = 0.016), stage II + III (HR = 2.17, P = 0.0015), stage III (HR = 2.09, P = 0.016), grade II (HR = 2, P = 0.0086), grade III (HR = 2.02, P = 0.023), Asians (HR = 2.57, P = 0.0022), alcohol consumption (None: HR = 1.81, P = 0.011), and hepatitis virus (Yes: HR = 1.79, P = 0.012) (Supplementary Fig. 1). In addition, higher SAMD13 expression correlated with poorer RFS in the white group (HR = 1.58, P = 0.046) (Supplementary Fig. 2). Higher SAMD13 expression correlated with poorer PFS in male (HR = 1.45, P = 0.043), grade I (HR = 4, P = 0.00067), white (HR = 1.57, P = 0.026), Asian (HR = 1.6, P = 0.048), and hepatitis virus (None: HR = 1.63, P = 0.028) (Supplementary Fig. 3). Higher SAMD13 expression correlated with poorer DSS in male (HR = 2.79, P = 0.00049), stage I + II (HR = 2.75, P = 0.0044), stage II (HR = 3.19, P = 0.042), stage II + III (HR = 2.12, P = 0.014), AJCC_T II (HR = 2.74, P = 0.039), white (HR = 2.23, P = 0.006), Asian (HR = 2.4, P = 0.028), alcohol consumption (None: HR = 2.15, P = 0.015), and hepatitis virus (Yes: HR = 1.95, P = 0.021) (Supplementary Fig. 4). The results showed that SAMD13 expression was correlated with the prognosis of HCC (Table 1).

Correlation between SAMD13 and clinicopathological characteristics in HCC

Clinicopathological characteristics Overall survival (n = 3,218) Relapse free survival (n = 2,809) Progression free survival (n = 3,162) Disease specific survival (n = 3,189)
N Hazard ratio P-value N Hazard ratio P-value N Hazard ratio P-value N Hazard ratio P-value
SEX
Male 246 2.63
(1.64~4.21)
3e-05 210 1.38
(0.92~2.05)
0.11 149 1.45
(1.01~2.07)
0.043 244 2.79
(1.53~5.09)
0.00049
Female 118 1.5
(0.85~2.66)
0.16 106 1.46
(0.81~2.64)
0.21 121 1.61
(0.96~2.7)
0.07 118 2.04
(0.98~4.25)
0.052
STAGE
I 170 1.45
(0.79~2.67)
0.23 153 1.16
(0.68~1.99)
0.59 171 1.18
(0.72~1.94)
0.51 168 1.63
(0.67~3.96)
0.28
I+II 253 1.82
(1.12~2.95)
0.014 228 1.36
(0.9~2.06)
0.15 256 1.45
(0.99~2.12)
0.053 251 2.75
(1.33~5.69)
0.0044
II 83 2.7
(1.16~6.27)
0.016 75 0.87
(0.45~1.69)
0.68 85 1.09
(0.61~1.97)
0.77 83 3.19
(0.98~10.4)
0.042
II+III 166 2.17
(1.33~3.54)
0.0015 145 1.24
(0.8~1.93)
0.34 170 1.25
(0.84~1.85)
0.28 166 2.12
(1.15~3.92)
0.014
III 83 2.09
(1.13~3.84)
0.016 70 1.77
(0.97~3.23)
0.062 85 1.4
(0.82~2.41)
0.22 83 1.64
(0.8~3.36)
0.17
III+IV 87 1.74
(0.97~3.11)
0.058 70 1.77
(0.97~3.23)
0.062 90 1.41
(0.83~2.39)
0.2 87 1.56
(0.78~3.13)
0.21
IV 4 - - 0 - 5 - - 3 - -
GRADE
I 65 2.05
(0.78~5.39)
0.14 55 1.96
(0.74~5.23)
0.17 55 4
(1.7~9.41)
0.00067 55 5.11
(1.29~20.22)
0.011
II 174 2
(1.18~3.38)
0.0086 149 1.38
(0.85~2.25)
0.19 177 1.36
(0.88~2.1)
0.16 171 2.03
(1.03~3.97)
0.036
III 118 2.02
(1.09~3.77)
0.023 107 1.28
(0.75~2.18)
0.37 121 1.29
(0.78~2.12)
0.32 119 1.78
(0.83~3.82)
0.13
IV 12 - - 11 - - 12 - - 12 - -
AJCC_T
I 180 1.37
(0.77~2.46)
0.28 160 1.16
(0.69~1.97)
0.57 181 1.18
(0.73~1.91)
0.49 178 1.4
(0.62~3.13)
0.41
II 90 2.52
(1.17~5.43)
0.015 80 0.91
(0.49~1.71)
0.77 93 1.15
(0.67~1.99)
0.61 91 2.74
(1.01~7.42)
0.039
III 78 2.12
(1.13~3.96)
0.016 67 1.59
(0.85~2.96)
0.14 80 1.25
(0.71~2.19)
0.44 77 1.72
(0.81~3.65)
0.15
IV 13 - 6 13 13 - -
Vascular invasion
None 203 1.46
(0.87~2.44)
0.15 175 1.09
(0.68~1.77)
0.71 205 1.05
(0.67~1.64)
0.83 201 1.37
(0.67~2.8)
0.38
Micro 90 1.5
(0.7~3.21)
0.29 82 1.13
(0.6~2.12)
0.71 92 1.25
(0.71~2.21)
0.43 90 1.44
(0.48~4.28)
0.51
Macro 16 - - 14 - - 16 - - 14 - -
RACE
White 181 1.55
(0.98~2.47)
0.059 147 1.58
(1~2.49)
0.046 184 1.57
(1.05~2.33)
0.026 179 2.23
(1.24~4)
0.006
Asian 154 2.57
(1.37~4.81)
0.0022 145 1.35
(0.82~2.24)
0.24 157 1.6
(1~2.57)
0.048 154 2.4
(1.08~5.37)
0.028
Alcohol consumption
Yes 115 1.27
(0.67~2.39)
0.46 99 0.94
(0.53~1.68)
0.84 117 1.07
(0.64~1.79)
0.79 117 1.14
(0.56~2.32)
0.72
None 202 1.81
(1.14~2.88)
0.011 183 1.43
(0.92~2.22)
0.11 205 1.47
(0.99~2.21)
0.057 199 2.15
(1.14~4.04)
0.015
Hepatitis virus
Yes 150 1.23
(0.64~2.34)
0.54 139 0.93
(0.57~1.53)
0.78 153 1.07
(0.67~1.69)
0.78 151 1.76
(0.77~4.02)
0.18
None 167 1.79
(1.13~2.84)
0.012 133 1.58
(0.95~2.6)
0.074 169 1.63
(1.05~2.53)
0.028 165 1.95
(1.09~3.47)
0.021

Fig. 3. The prognostic significance of SAMD13 expression levels. The prognostic significance of SAMD13 was identified according to gender, race, age, and stage.

Correlation between SAMD13 and infiltrating immune cells in HCC

We explored the correlation between SAMD13 and infiltrating immune cells in HCC by using the TIMER database. The results revealed that SAMD13 significantly and positively correlated with the infiltration levels of CD8+ T cells (R = 0.045, P = 4.09e-01), CD4+ T cells (R = 0.214, P = 6.40e-05), B cells (R = 0.162, P = 2.52e-03), neutrophils (R = 0.106, P = 4.93e-02), macrophages (R = 0.272, P = 2.85e-07), and dendritic cells (R = 0.304, P = 8.46e-09) in HCC (Fig. 4A). Moreover, we investigated the relationship between each infiltrating immune cell type and HCC prognosis. High SAMD13 expression and cell infiltration levels (CD8+ T cells, CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells) were associated with worse prognosis than low SAMD13 expression and cell infiltration levels (Fig. 4B). Therefore, high SAMD13 expression is related to infiltrating immune cells and may affect tumor prognosis.

Fig. 4. Correlation between SAMD13 and infiltrating immune cells in HCC. (A) The correlation between SAMD13 and infiltrating immune cells was identified. (B) The prognostic value between SAMD13 and infiltrating immune cells was identified.

SAMD13 Co-expression network in HCC

To explore the biological functions involving SAMD13 in HCC, the LinkedOmics module was used to obtain the co-expression patterns of SAMD13. In total, 6804 genes showed a negative correlation with SAMD13, while 13110 genes showed a positive correlation (Fig. 5A). The heatmaps showed that the top 50 genes were positively and negatively associated with SAMD13 (Figs. 5B, C). GO biological analysis indicated that the genes co-expressed with SAMD13 mainly participated in protein localization to chromosomes, rRNA metabolic processes, ncRNA processing, ribonucleoprotein complex biogenesis, and negative chemotaxis (Fig. 5D). Furthermore, the SAMD13 pathway analysis indicated that the co-expressed genes of SAMD13 were mainly enriched in the spliceosome, ribosome biogenesis in eukaryotes, ribosomes, aminoacyl-tRNA biosynthesis, and homologous recombination (Fig. 5E). SAMD13 showed a positive association with expression of E2F5 (R = 0.4186, P = 9.562e-17), YBX1 (R = 0.3942, P = 7.148e-15), PDCL3 (R = 0.3879, P = 2.086e-14), PLBD1 (R = 0.3843, P = 3.792e-14), SRI (R = 0.38, P = 7.572e-14), TMEM67 (R = 0.3751, P = 1.67e-13) (Fig. 6). Therefore, SAMD13 may have a significance in the prognosis of HCC.

Fig. 5. Co-expression genes of SAMD13 in HCC. Co-expression genes of SAMD13 were analyzed using the LinkedOmics database. (A) Highly correlated genes of SAMD13 were tested by the Pearson test. Heat maps presenting top 50 genes correlated with that of SAMD13 in HCC. Red indicates positively correlated gene (B) and blue indicates negatively correlated gene (C). Biological process enrichment analysis of C4orf47 co-expressed genes (D). KEGG pathway analysis of C4orf47 co-expressed genes (E).
Fig. 6. Correlation with positive-related genes of SAMD13 in HCC. The positive-related genes of SAMD13 were identified using the LinkedOmics database.

The prognostic significance of SAMD13 positive-related gene in HCC

Thirty-seven genes had a high HR (P < 0.05) for OS, and 18 genes had high HR for DFS (Fig. 7A). In contrast, 14 of the negative genes had a low HR (P < 0.05) for OS, and seven genes had a low HR for DFS (Fig. 7B). Moreover, we found that the positive- and negative-related genes of SAMD13 showed high and low HR in diverse types of cancer, respectively (Supplementary Fig. 5).

Fig. 7. The prognostic significance of SAMD13-related genes in HCC. Survival map of the positive-related genes of SAMD13 in OS and DFS (A). Survival map of the negative-related genes of SAMD13 in OS and DFS (B).
DISCUSSION

Liver cancer, with high tumor-related mortality worldwide, is divided into three major pathological types: HCC, intrahepatic CC, and combined HCC/CC. It shows differences in pathogenesis, histological form, treatment, and prognosis. As HCC is initially asymptomatic and diagnostically challenging in the early stages, it is diagnosed in the later stages (progressive state) of HCC (Kulik and El-Serag, 2019; Wallace et al., 2015) As a result, the prognosis after HCC diagnosis is very poor. Therefore, there is an increasing need for research on biomarker development that can aid in early diagnosis.

Cells of the immune system that play an important role in controlling cancer progression, can also accelerate cancer progression (Goswami et al., 2017; Ostrand-Rosenberg, 2008). For example, TIICs aid in the growth of cancer cells and tumors (Bremnes et al., 2011). TIICs and tumors are closely related to clinical outcomes and help predict patient responses before cancer treatment (Choi et al., 2017). Over the years, HCC-related TIICs have been identified as having the prognostic value of immune molecules (Harding et al., 2019; Ma et al., 2018; Sun et al., 2019; Tian et al., 2019; Yang et al., 2019).

We studied the expression levels of SAMD13 in HCC using the TIMER and UALCAN databases. Differential expression of SAMD13 between tumor and normal tissues has been observed in a variety of cancers. We showed that SAMD13 expression was higher in HCC than in normal adjacent tissues. Based on the UALCAN database, SAMD-13 expression was higher in primary tumors, cancer stage (I, II, III, IIII), tumor grade (I, II, III, IIII), and nodal metastasis status (N0) in HCC. Based on these results, we identified the prognostic significance of high SAMD13 expression in HCC. Analysis of the KM plotter, Oslihc, and GEPIA revealed that high expression of SAMD13 correlated with poor prognosis in HCC. We found that high expression of SAMD13 correlated with a high HR for poor survival rate. In addition, the TIMER database identified that high SAMD-13 expression was correlated with poor prognosis in male patients, stages, grades, race, alcohol consumption, and hepatitis virus infection in HCC. These results suggest that SAMD13 may be a prognostic biomarker for HCC.

An important aspect of our study was that SAMD13 expression correlated with the level of immune infiltration in HCC. We confirmed that the infiltration levels of CD8+ T cells, CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells were correlated with SAMD13 expression levels. Moreover, we showed that high SAMD13 expression levels correlated with poor prognosis of infiltrating immune cells. The results showed that high SAMD13 expression and high CD8+ and CD4+ T cell infiltration levels were associated with a worse prognosis than low SAMD13 expression and low CD8+ and CD4+ T cell infiltration levels. High SAMD13 expression and high B cell infiltration levels had a worse prognosis than low SAMD13 expression and low B cell infiltration levels. High SAMD13 expression and high neutrophil infiltration levels had a worse prognosis than low SAMD13 expression and low neutrophil infiltration levels. High SAMD13 expression and high macrophage infiltration levels had a worse prognosis than low SAMD13 expression and low macrophage infiltration levels. High SAMD13 expression and high dendritic cell infiltration levels had a worse prognosis than low SAMD13 expression and low dendritic cell infiltration levels. These results suggest that high SAMD13 expression and immune cell infiltration are associated with poor prognosis.

By analyzing the SAMD13 co-expression network, genes with positive and negative correlations were identified. SAMD13 has been confirmed to affect the prognosis of patients with HCC. SAMD13-related positive genes indicated that 37 genes had a high HR for OS, and 18 genes had a high HR for DFS. SAMD13-related negative genes indicated a low HR of 14 genes in OS, and 7 genes indicated a low HR in DFS. According to the biological functions of SAMD13 the in HCC, we identified that the functional consequences of SAMD13 mainly include protein localization to chromosomes, rRNA metabolic processes, ncRNA processing, ribonucleoprotein complex biogenesis, and negative chemotaxis, while it inhibits processes including peroxisome organization, peroxisomal transport, tricarboxylic acid metabolic process, and regulation of carbohydrate metabolic processes.

In conclusion, we showed that high SAMD13 expression is associated with poor prognosis and tumor infiltration of immune cells in HCC. Therefore, this study demonstrates the significance of SAMD13 as a novel prognostic biomarker for HCC. These results can be used as data for identifying potential targets of immunotherapy in HCC. These data may help understand the role of SAMD13 in various cancers, including HCC, and further studies should be undertaken to outline its detailed mechanisms.

ACKNOWLEDGEMENT

This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (RS-2022-00165637, NRF-2021R1C1C1003333).

Footnote

bsl-28-4-260-supple.pdf

CONFLICT OF ENTEREST

Authors declare no competing interests.

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